Hybrid Metaheuristics: 6th International Workshop, Hm 2009 Udine, Italy, October 16-17, 2009 Proceedings (lecture Notes In Computer Science)

This book constitutes the refereed proceedings of the 6th International Workshop on Hybrid Metaheuristics, HM 2009, held in Udine, Italy, in October 2009. The 12 revised full papers presented together with one invited talk were carefully reviewed and selected from 22 submissions. The papers discuss current issues of combinations of metaheuristics and other solving techniques of universal concern such as novel combinations of components from different metaheuristics, hybridization of metaheuristics and AI/OR techniques, low-level hybridization, high-level hybridization, portfolio techniques, expert systems, cooperative search, automated parameter tuning, empirical and statistical comparison, theoretical aspects of hybridization, parallelization, and software libraries. The International Workshop on Hybrid Metaheuristics was established with the aim of providing researchers and scholars with a forum for discussing new ideas and research on metaheuristics and their integration with techniques typical of other ?elds. The papers accepted for the sixth workshop con?rm that such a combination is indeed e?ective and that several research areas can be put together.Slowlybut surely,thisprocesshasbeen promotingproductivedialogue amongresearcherswithdi?erentexpertiseanderodingbarriersbetweenresearch areas. The papers in this volume give a representativesample of current researchin hybrid metaheuristics. It is worth emphasizing that this year, a large number of papers demonstrated how metaheuristics can be integrated with integer linear programmingandotheroperationsresearchtechniques.Constraintprogramming is also featured, which is a notable representative of arti?cial intelligence solving methods. Most of these papers are not only a proof of concept – which can be valuable by itself – but also show that the hybrid techniques presented tackle di?cult and relevant problems. In keeping with the tradition of this workshop, special care was exercised in the review process: out of 22 submissions received, 12 papers were selected on the basis of reviews by the Program Committee members and evaluations by the Program Chairs. Reviews were in great depth: reviewers sought to p- vide authors with constructive suggestions for improvement. Special thanks are extended to the Program Committee members who devoted their time and - fort. Special gratitude is due to Andrea Lodi and Vittorio Maniezzo, who both accepted our invitation to give an overview talk. Front Matter....Pages - Hybrid Metaheuristic for the Assembly Line Worker Assignment and Balancing Problem....Pages 1-14 An ELSxPath Relinking Hybrid for the Periodic Location-Routing Problem....Pages 15-29 Hybridizing Beam-ACO with Constraint Programming for Single Machine Job Scheduling....Pages 30-44 Multiple Variable Neighborhood Search Enriched with ILP Techniques for the Periodic Vehicle Routing Problem with Time Windows....Pages 45-59 A Hybridization of Electromagnetic-Like Mechanism and Great Deluge for Examination Timetabling Problems....Pages 60-72 Iterative Relaxation-Based Heuristics for the Multiple-choice Multidimensional Knapsack Problem....Pages 73-83 Solving a Video-Server Load Re-Balancing Problem by Mixed Integer Programming and Hybrid Variable Neighborhood Search....Pages 84-99 Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling....Pages 100-114 Hierarchical Iterated Local Search for the Quadratic Assignment Problem....Pages 115-129 Incorporating Tabu Search Principles into ACO Algorithms....Pages 130-140 A Hybrid Solver for Large Neighborhood Search: Mixing Gecode and EasyLocal + + ....Pages 141-155 Multi-neighborhood Local Search for the Patient Admission Problem....Pages 156-170 Matheuristics: Optimization, Simulation and Control....Pages 171-177 Back Matter....Pages - The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling